While our framework is general we demonstrate it on the \(k\) -means problem and its variant. There are at least three reasons for this: (i) This is a fundamental problem in both computer science and machine learning, (ii) This is probably the most common clustering technique that used in practice, (iii) Many other clustering and non-clustering problems can be reduced to \(k\) -means; e.g. Mixture of Gaussians, Bregman Clustering, or DP-means [1]}, [2]}, [3]}. In this context we suggest offline coreset constructions for \(k\) -clustering queries, that can be constructed in a streaming fashion using our streaming approach, and are:
